The Top Challenges with Sentiment Analysis

Did you know that there are more than 200 tools and platforms that can help you track and assess how many times your business or brand has been mentioned in social media channels?

And many of these listening platforms do more than just the basic monitoring. In fact, they now offer integrated approaches to get the right information to the right parts of your business: product development; customer support; public outreach; lead generation; market research; and campaign measurement.

It’s a big responsibility and commitment to listen to your customers. That’s why businesses are making it a priority to enhance their social monitoring efforts and sentiment analytics to pick up and decipher what their customers are saying about them.

Still, it’s important for data scientists to use caution when accepting customer statements at face value since context has such a great bearing on meaning. Analyzing natural language is difficult enough. Sarcasm or other forms of derisive language are extremely problematic for technologies to interpret.

For instance, let’s say Karen learns from a Facebook friend that an electronics company has just started charging customers a support fee for a popular product that had historically been free. Karen posts the following response on Facebook: “Oh, that’s just great.”

Taken literally, or by narrowing the analysis to positive or negative words that are made about the electronics company in social media, Karen’s statement would be interpreted to mean that she’s pleased by the change in the support policy. But more than likely, she’s simply being sarcastic.

In many cases, analytics teams are evaluating larger samples of customer statements to help spot potential product issues or indicators that could signal customer churn.

They also do this to help identify possible trends within different customer segments. It’s not cost effective nor efficient for data scientists to analyze the sentiments of individual customers, with the possible exception of companies that market a limit number of high-end products such as luxury shoes.

Taking this a step further, let’s say an automotive maker uses Twitter to analyze comments that are made about its competitors. Taken on its own, such an analysis will capture the opinions of a subset of Twitter users. But it’s not necessarily a fair representation of the universe of Twitter users.

Sentiment analysis tools continue to evolve and will continue to improve over time. In the end, organizations that augment sentiment analysis with analysts who are able to interpret context in comments and take comprehensive approaches to sentiment analysis are those that are likely to benefit most.

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Sentiment analysis is, to be sure, an imperfect technology. Yet perfection isn’t usually a requirement for profit.

Take the example of direct marketing. The best direct marketers are constantly testing their copy, design, even the color of their envelopes to discover what results in the best returns for their business. This is true for both traditional mail and online direct marketing, except that there are no envelopes on the internet.

Adding sentiment analysis to that mix, even imperfect as it is, could help identify the good prospects, the best copy to use and so forth. It doesn’t have to be perfect, just good enough for the returns to make the analysis look like a bargain.

Getting positive ROI for text analytics is a pet issue of mine. If you’re interested in this topic, you might like my article, Text Analytics WIIFM (What’s in it for Me?), http://bit.ly/smartdata017.

Sentiment monitoring and analysis is also used for product planning, customer service, risk management, reputation management and investment planning. The scope of data sources collected and historical time frame are key to a comprehesive platform; as is the ability to generate “white labeled” reports, extract data and provide an API for application integration.

[...] forms of derisive language are extremely problematic for technologies to interpret,” notes a post on the TIBCO Business Intelligence Blog. Context could be another problem. How well would sentiment analysis tools differentiate between [...]